code: def test_estimates():
      mod = RecursiveLS(endog, exog)
      res = mod.fit()
  
      # Test for start_params
      assert_equal(mod.start_params, 0)
  
      # Test the RLS coefficient estimates against those from R (quantreg)
      # Due to initialization issues, we get more agreement as we get
      # farther from the initial values.
      assert_allclose(
          res.recursive_coefficients.filtered[:, 2:10].T,
          results_R.iloc[:8][["beta1", "beta2"]],
          rtol=1e-5,
      )
      assert_allclose(
          res.recursive_coefficients.filtered[:, 9:20].T,
          results_R.iloc[7:18][["beta1", "beta2"]],
      )
      assert_allclose(
          res.recursive_coefficients.filtered[:, 19:].T,
          results_R.iloc[17:][["beta1", "beta2"]],
      )
  
      # Test the RLS estimates against OLS estimates
      mod_ols = OLS(endog, exog)
      res_ols = mod_ols.fit()
      assert_allclose(res.params, res_ols.params)
  
imports: import os
  import numpy
  from numpy.testing import assert_allclose, assert_equal, assert_raises
  import pandas
  import pytest
  from scipy.stats import norm
  from statsmodels.datasets import macrodata
  from statsmodels.genmod.api import GLM
  from statsmodels.regression.linear_model import OLS
  from statsmodels.regression.recursive_ls import RecursiveLS
  from statsmodels.stats.diagnostic import recursive_olsresiduals
  from statsmodels.tools import add_constant
  from statsmodels.tools.eval_measures import aic, bic
  from statsmodels.tools.sm_exceptions import ValueWarning
label_desc: Use image node_id 6 for calling a global function with example usage: test_estimates() without return types